Turning Your Building into a Battery to Reduce Energy Costsqcoefficient.com/assets/documents/White Paper- Turning Buildings I… · Turning Your Building into a Battery to Reduce

Turning Your Building into aBattery to ReduceEnergy Costs

Commercial and institutional buildings have inherentthermal capacity to store energy. The trick is how tointegrate building operations with electric grid operationsto leverage a building’s thermal mass and drastically reduceenergy expense.

Clean Urban Energy, Inc.

233 South Wacker Drive

Suite 6225

Chicago, IL 60606

www.cleanurbanenergy.com

Clean Urban Energy, Inc. Turning Buildings into Batteries™ 1 | P a g e

Abstract

Throughout the U.S. and in many parts of the world, there is a significant delta between energy

consumed during on peak times and off peak times. Because there has traditionally been no effective

way to store electricity, it must be generated, distributed and used in sync in real time. Upgrades to the

generation and distribution infrastructure are costly and difficult to justify when additional capacity may

only be needed to support peak loads. As a result, the task of satisfying peak demand presents a

challenge for everyone along the electric supply chain.

Grid constraints during peak times impact our economy in many ways; from higher energy costs for

customers to increased CO2 emissions for generators, reliability concerns for utilities and excessive risk

for competitive electricity suppliers. Each participant in the electric supply chain is vulnerable to peak

demand problems. The result: billions of dollars of cost to our economy annually.

Utilities and grid operators have attempted to address the issue with incentive programs, rate

structures, and awareness programs. In California, utilities provide what is called Technical

Assistance/Technology Incentives programs (TA/TI) to provide customers funding to analyze and

implement demand reduction strategies. For example, Southern California Edison (SCE) encourages

commercial customers to participate in demand response programs by offering free technical audits and

demand response equipment. Many utilities also have Permanent Load Shifting (PLS) programs,

incentivizing building owners to implement expensive Thermal Energy Storage measures like ice plants,

fuel cells, and lithium ion batteries.

Government organizations have also recognized the severity of our peak demand problem and

attempted to address with energy policy and grants. In 2009, the US government awarded $3.4 billion

dollars towards smart grid projects though the American Recovery and Reinvestment Act (ARRA) and

the US Department of Energy (DOE) announced and additional $620 million to fund new technologies

such as grid-scale energy storage.

Energy policy has also been used to address the issue. In 2011, the Federal Energy Regulatory

Commission announced Order 745, which will make load reduction equivalent to incremental supply on

wholesale markets. Once implemented, this will provide a powerful incentive for electricity consumers

to reduce consumption at peak times.

Unfortunately, there are limitations with each of these solutions. This paper will discuss those

shortcomings and introduce a new solution to relieve grid constraints.


Current Situation

Peak electric demand is a pressing issue for

consumers, suppliers, and generators. Generators

and energy suppliers are required to have enough

electric capacity to satisfy demand at all times.

However, consumers are rarely operating at peak

levels. Figure 1 shows how the aggregate load in

California peaks for a very short period of time in

the afternoon, leaving significant electricity

infrastructure idle for much of the day.

The enormous disparity between on-peak and off-

peak electric consumption can be explained using

load factor. Load Factor is a measure of how flat or consistent a load profile is. It is a percentage of

time that a building is near its peak. A typical commercial or institutional building has a load factor of

.55, meaning its peak electric consumption occurs for a very short period of time. When consumers are

not operating at peak, the grid has excess capacity and unfortunately, there is little use for electricity

during pre-dawn hours. If electricity could be efficiently stored like natural gas, electricity use could be

shifted to off-peak hours and a building’s load factor would be closer to 1.0. This would significantly

reduce grid congestion and provide tremendous societal benefit in the form of reduced emissions and

lower costs.

As utilities have built new capacity over the years, they have retired older, less efficient generating

plants. These plants are mainly used to serve the peak load requirements. They are old, inefficient, and

environmentally problematic. As these “peaker” plants are being torn down, utilities need to build new

generating capacity to meet demand. It takes several years to plan and build power plants and

transmissions and distribution systems and several millions of dollars.

There is often public opposition to new infrastructure due to the NIMBY (Not in My Back Yard)

philosophy. We, as consumers, want reliable and inexpensive power, but don’t want the transmission

lines running through our back yards. This was especially evident in the years leading up to the

California Energy Crisis. Metcalf Energy, for example, waited for approval to build a 600-MW

combustion-turbine generating plant near Morgan Hill, California. Though the Sierra Club and the

American Lung Association endorsed the construction, CISCO Systems was a major opponent. Though

CISCO was a part of an industry that relied heavily on electricity for communications and manufacturing,

they did not want a power plant next to their office park. It is a catch 22 situation.

Figure 1


Suppliers and utilities face a huge risk when peak soars because they either need to buy on the spot

market in real time when prices are high, or spend significant funds on hedging to insulate themselves

from the risk. Consumers want cost predictability almost as much as low prices, often feeling they can

absorb increases in costs so long as they are aware of the increase beforehand. When suppliers have to

provide flat energy prices to their customers yet buy on wholesale markets that are not flat, they are

exposed to risk. They have obligations to provide power and positions in the market to meet those

obligations, but are forced to either buy excess power or derivatives to cover their risk. These costs are

included in the flat rate they provide consumers.

Many times, the power plants that are being used to deliver peak load are inefficient, outdated plants.

They have less efficient transmission paths, and emissions from those plants are higher than that of the

base load plants. Utilities and generators are penalized for emissions and the higher the emissions, the

higher their penalties. Many times these penalties are passed through to consumers in rates, costing

consumers millions of dollars annually.

The result is that electric customers get high electric bills, either on the supply portion of their bill or on

the delivery of the power. These charges can be seen in peak demand and peak consumption fees.

Problems/Shortcomings with Current

There are several solutions that have been introduced to address the peak load congestion. In some

cases, these solutions have provided incremental improvements but have failed to sufficiently address

the whole problem. The following describes these approaches and associated deficiencies.

Traditional

Approach Description Shortcomings

Demand

Response

Demand Response is a program wherein buildings lower peak demand in “response” to a market condition. The market condition is typically a curtailment request from the ISO or utility. Building owners can sign up for a DR program and either volunteer or commit to certain reduction levels.

Demand response programs have limitations in that building owners and managers often lack the technology to implement demand response, don’t have sufficient knowledge of risks and opportunities (costs and benefits analysis), and lack transparency to energy markets. As a result, adoption of demand response in commercial buildings has not met expectations.


Thermal

Energy

Storage in

Ice Plants

Thermal energy storage – ice plants are a great way to take advantage of inexpensive off peak energy. A building would build an ice plant to make ice during the middle of the night when prices are low and then use the ice throughout the day to reduce chiller plant energy consumption during peak times.

Thermal energy storage has shown to have a significant savings impact in many markets that have a high spread between on-peak and off-peak prices and many times utilities offer incentive programs for having the plant installed. Unfortunately, an ice plant requires capital investment-- typically over $1 million (approximately $1000/kW). In addition to those costs, space to locate the plant and tanks and associated operating costs are prohibitive for most buildings. Chilled water storage requires about 20 to 30 cubic feet per ton-hour, and ice storage requires about 3 to 4 cubic feet per ton-hour. As a result, this strategy has been limited to areas of the country that have the highest peak rates and largest customer buildings or campuses.

Distributed

Generation

Onsite generation can be used to flatten load but requires capital investment.

There are several methods of on-site generation available to commercial office buildings. Solar panels and wind are the most common. The cost per Watt of solar power is about twice that of coal generation. Because solar panels can last from 25 to 40 years, return on investment is typically realized at between 8 and 12 years. While photovoltaic installations have proven effective on large, warehouse type buildings with broad, flat roofs, they are not as useful on urban, commercial office buildings simply because there is not enough flat, sun-exposed area on which to mount them.

On-site gas generators are useful in hospitals and industrial sites, where steam created during generation can be recycled to serve another purpose. They are frowned upon in grid-congested urban areas because they produce steam and pollutant emissions. Diesel generators produce the highest amounts of nitrogen oxides, and are only permitted to provide emergency electricity.

Utility Rate

Design Rates – Time of Use and demand charges.

Utility rates often charge more for on-peak usage than off-peak and often carry demand charges and ratchets that constitute a large percentage of the total electricity cost. Customers lack solutions that enable them to adjust their operation/building to manage on-peak usage.


Solution – Buildings as Batteries

The traditional solutions pose fundamental flaws. Thermal energy storage is expensive, demand

response is not sophisticated, and utility rates are static. Each was intended to address an underlying

problem: lack of efficient method to store electricity. To truly solve the peak load problem, building

operations must be integrated to grid operations with complete transparency between the systems.

There are three basic components that will enable this integration: 1) the building must have an

effective method to reduce load without expensive capital investments; 2) the grid and building must

communicate and have intelligence; and 3) the building needs the ability to predict load requirements to

implement changes in advance of congestion events.

1. Consumers need an inexpensive method to reduce load. If only a select few buildings can afford

to implement the solution, the aggregate benefit will not solve grid constraints. Fuel cells, ice

plants, and onsite generation are expensive and require significant capital. Utilizing existing

systems-- building automation, metering, building structure-- would provide a cost effective

solution for all buildings and budgets.

2. The electric grid and commercial buildings must communicate seamlessly and have intelligence

so buildings can dynamically respond to, or work within, the operation of the grid. The building

needs to know when the grid is constrained and needs help, and the grid needs to know what it

can count on from a building in the way of load reductions. The grid and the building need the

real time ability to dynamically adjust to what the real time requirements are.

3. The grid and building need to know in advance what the conditions will be and have ability to

identity and collaboratively implement a solution to avoid a condition versus respond to the

condition. This requires a predictive tool that can see future energy prices, weather patterns,

and building impact.

With these three areas addressed, grid constraints can be drastically reduced, which will save consumers

money on demand charges and TOU rates, while increasing DR revenue.

How it Works

All buildings have an inherent capacity to store energy in their thermal mass. Some buildings have more

efficient thermal mass than others, with components such as building materials, insulation, window

specification, tenant use, and solar position impacting the thermal mass efficiency. The trick is to

determine the efficiency of the thermal mass—to measure its ability to store energy-- which in turn

impacts heat transfer between surfaces and air throughout the day. For example, if the space


temperature of a room is 73 degrees and the mass (concrete, drywall, books, desks, etc.) is 69 degrees

due to cool thermal energy being stored there earlier in the day, then there is a positive heat transfer of

4 units between the thermal mass and the air. This heat transfer takes heat out of the air and allows the

space to remain comfortable long after the mechanical cooling has been reduced.

The process starts by capturing data about the building such as floor area, construction materials,

window specifications, mechanical systems, ceiling materials and construction, and interior furnishings.

Also important is the position of building to the sun. The model includes hours of operation, number of

tenants, percentage of conditioned space, and more. Finally, coupling of thermal mass to space air and

decoupling of outside air to thermal mass is a critical component to modeling buildings and the Energy

Plus model includes such analysis. Data is input into an online Building Survey and integrated into

EnergyPlus, a Department of Energy whole building modeling tool that engineers use to design buildings

and optimize energy and water use.

Once the building has been modeled to

determine the thermal capacity of the mass,

the building can integrate ongoing

operational data from the building

automation system, hourly weather forecasts,

and energy market information such as day

ahead or real time pricing, into the platform.

In markets that do not have dynamic pricing, the published rate would be used. The platform

algorithms simulate different load profiles to determine optimal times to cool the building. For

example, if energy prices are low between 4:00 am and 6:00 am and the outside temperature is 60

degrees with low humidity, then the system will precool the building using free outside air versus

mechanical cooling. By cooling during unoccupied hours, the building mass is sufficiently cooled and will

provide favorable heat transfer in the building that will last most of the day.

Thermal Comfort

Maintaining a comfortable workplace is a critical component in facility management. Thermal comfort

will generally not be compromised for energy savings, as the adverse impact on productivity typically far

outweighs the gains from energy savings. Performing load shaping has traditionally impacted comfort as

precooling delivers uncomfortably cool temperatures in the morning hours and peak load shedding

often entails letting temperatures float to uncomfortably warm levels later in the day. Dynamically

shaping load throughout the day without impacting tenant comfort is accomplished by leveraging the

thermal mass.

Figure 2


ASHRAE 55 is a building standard that specifies the combinations of

indoor thermal environmental factors and personal factors that will

produce thermal environmental conditions acceptable to a majority

of the occupants within a space. The environmental factors

addressed in the standard are temperature, thermal radiation,

humidity, and air speed; the personal factors are those of activity

and clothing.

In the standard, ASHRAE introduces Predictive Mean Vote (PMV), which is an index that predicts the

mean value of votes of a large group of people. PMV is essentially a prediction of how many occupants

in a space will be comfortable. A PMV of 0 is a thermally neutral sensation, which is the lowest portion

on the curve in Figure 3, while positive and negative scores are too warm and too cold respectively. In

the region between plus and minus .5, thermal comfort is not compromised. In a test by Morris et al,

precooling and changing PMV by -.5 reduced peak demand by 40%, which shows that minor

temperature adjustments can have a big impact on peak reductions without sacrificing comfort. By

leveraging thermal mass, CUE can be more aggressive in load shaping by storing and releasing energy

throughout the day, delivering even greater savings.

Maintaining control of a building is very important to building owners and facility managers. Although

CUE is a SaaS platform that sends commands to the buildings automation system, the commands are

limited to space temperature setpoints that are within predefined thresholds set by the building

operator. As the automation system receives the setpoints throughout the day, it initiates control

sequences to deliver the hourly temperature requirements. The CUE algorithms work within the

temperature bands given by the building operator. There is no manipulation of control sequences or

PID loops.

CUE provides a dynamic load shaping

solution that takes inputs from three

sources: electric markets, weather, and

buildings. Algorithms run in the NOC

throughout the day, as shown in Figure 4,

and deliver optimal space temperatures

back to the building. In doing so, it

becomes an operational tool that initiates

energy savings every day, every hour. No

manual intervention is required and

because the platform is a cloud application,

there is no capital investment for building

owners.

Figure 3

Figure 4


Case Study

CUE implemented its SaaS platform in a

976,000 ft² office building in downtown

Chicago. The building’s management team

hoped that CUE technology would achieve

energy expense savings, promote energy

efficiency, and maintain the comfort of their

tenants. In the first month of operation, the

CUE technology achieved all of these goals.

Using CUE technology, the building reduced

peak demand costs by 30% and daily on-peak

energy consumption by up to 30%, increasing

demand response revenue, and making the

HVAC system more efficient with its energy

use. As Figure 5 shows, the “pre-CUE” HVAC demand profile on two days, represented by the green and

blue lines, had a high peak load. After implementing the CUE Thermal Energy Storage solution, the peak

demand and on-peak consumption was dramatically reduced, as is indicated with the black trend line.

The building manager maintained control of zone temperature setpoints and was able to achieve these

reductions without impacting tenant comfort. Additionally, CUE technology found untapped efficiencies

in the building’s chiller operations. While this building typically operated four chillers every day, they

now only needed three.

Summary

Constraints on the electric grid are a growing problem globally. Demand is increasing, plants are being

retired, and transmission infrastructure is dated. The imbalance between supply and demand during

peak times causes problems for all entities throughout the supply chain. For generators, peak plants are

typically less efficient and costly to operate and may incur emission penalties. Competitive electric

suppliers are exposed to significant risk associated with peak loads and spend millions annually to

manage that risk. Finally, consumers are subjected to high prices and potential brownouts. There have

been numerous technological and legislative solutions introduced, rates altered, and utility programs

implemented. Each has shortcomings. Clean Urban Energy provides a solution to grid constraints by

utilizing a building’s inherent thermal mass as an energy storage medium to allow electricity to be used

when prices are low, maintaining tenant comfort by releasing energy from mass throughout the day.

Because algorithms do the optimization with inputs from energy markets, weather forecasts, and

Figure 5


building systems, no manual intervention is required by building operators. It requires no capital

investment, no expertise or unique intellectual aptitude, and no sacrifice to tenant comfort.

Buildings as Batteries, managed by a sophisticated SaaS platform, solve the peak load problem and

overcome the challenges other intended solutions encounter. The result, significant savings and

community benefit.

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